Watch the video of this project, which features the participants who have a BRCA mutation and their interaction with the piece. The video also highlights the design and construction of the mural.
I recently took part in a deeply meaningful collaboration of science, art and personal stories of cancer survivors.
Together with Joanna Rudnick and Aaron De La Cruz, we sought to create a work of art that combines the science of cancer genomics and the individuals whose lives are affected by genetic mutations in the BRCA1 and BRCA2 genes, where genomic changes drastically increase one's chances of breast and ovarian cancer.
We wanted to make something that is scientifically accurate, artistically beautiful and emotionally engaging. The complexity of the genome, the multitudes of other genes and possible mutations and the millions of personal stories of hardship and survival were just a few of the elements we wanted to include the the piece.
My role was to provide the scientific direction behind the design and incorporate it into the aesthetic of Aaron De La Cruz, a street artist from San Francisco whose work echoes information, complexity, interaction and continuity. We all have a genome — a different genome. The ways in which our genomes are different is what gives us traits like hair and eye color, but is also what makes some of us predisposed to diseases like cancer.
The mural, which includes elements drawn by the cancer survivors, is part of the Free the Data campaign, which is advocating for an open access model of genome mutation databases so that scientists everywhere can analyze it and help women make informed choices about their breast-cancer risk.
Imagine you are a physician or researcher and seek to get more confirmation on the clinical impact of particular genetic variants. If your search of public databases comes up empty this does not necessarily mean that nothing is known about the mutations in question. Rather, the information may be locked away as a trade secret in a genetic testing company’s proprietary database.
The New York Times article DNA Project Aims to Make Public a Company’s Data on Cancer Genes captures the current state of the situation.
The mural was constructed on location at InVitae in San Francisco.
This work will be, as far as I know, the first human annotation of mutations in the human genome by humans whose genomes have the mutations. That's quite a term!
I've always been mindful of the necessity of the mingling of art and science. In my work I tried to add things I felt about the science I thought to create work that combines our objective understanding of the world we live in with the subjective experience of living in it. This project, by far, has been the most keenly felt.
The mural was created in San Francisco on Saturday, July 13th, 2013. We are starting with a 11' x 6' wood canvas. These dimensions reflect the ratio of lengths of BRCA1 and BRCA2 proteins (1,863 and 3,418 amino acids, respectively)
The BRCA1 and BRCA2 proteins are drawn on the canvas as straight-line sections.
The locations of the participants mutations are positioned on the protein lines as circles. For individuals with large deletions, the circle is placed at the first affected amino acid. Because BRCA1 is location on the opposite strand (anti-sense), its start on the canvas is on the right.
The rest of the genome is now drawn. Aaron's style is perfect for depicting information and the endless complexity of the genome and its interacting elements. We were careful to include elements that indicate that the story told today is not complete. Millions of others have mutations in thousands of other genes, each potentially life-threatening. Just as the stories of our participants will continue to evolve, other stories are waiting to be told.
Once the "reference" genome is depicted, participants with BRCA1 and BRCA2 mutations will complete the art work by individually marking the positions of their mutations on the art using personalized colors. With Aaron's help, everyone created their own color by mixing primary colors.
From base pair, to genome, to person, to life. All it takes is one tiny change in the genome to change a life forever.
The BRCA1 and BRCA2 lines were placed on the canvas by first pinning two pieces of string, marked with the positions of the mutations.
After drawing the protein lines, it was time to fill the canvas.
Over the next 4 hours, Aaron filled in the canvas with the "rest" of the genome.
Cancer previvors and survivors who have been diagnosed with a mutation on BRCA1 or BRCA2 genes.
Joanna made her directorial debut with the Emmy-nominated In the Family, a deeply personal film about coming to terms with testing positive for the breast cancer gene BRCA1 mutation and following the storylines of other women and families facing the same hard choices. In the Family premiered at Silverdocs in 2008, was broadcast nationally on PBS P.O.V. the same year and was a finalist for the NIHCM Foundation’s Health Care Radio and Television Journalism Award.
Joanna received a master’s degree in Science and Environmental Journalism from New York University and a bachelor’s degree in English from Northwestern University. Joanna loves the opportunity to teach and mentor and served as an adjunct professor at Northwestern University’s Medill School of Journalism in the past.
She has written for several publications including Audubon Magazine, The Artful Mind, The Berkshire Record and Humanities. Before finding her way to the wonderful world of documentaries, Joanna served as an Americorps volunteer, implementing project-based environmental curricula in the San Francisco Public School System.
Joanna is one of the cancer survivors whose mutations are encoded in the art.
Aaron De La Cruz's work, though minimal and direct at first, tends to overcome barriers of separation and freely steps in and out of the realms of design, graffiti, and illustration.
The parameters he has chosen to work within actually allow him to free himself and react to the very limitations he has created. This overriding structure and the lack of deliberation while moving within creates a tension when encountering his work due to the almost computer generated grid like systems he creates by unplanned markmaking. The act and the marks themselves are very primal in nature but tend to take on distinct and sometimes higher meanings in the broad range of mediums and contexts they appear in and on.
His work finds strengths in the reduction of his interests in life to minimal information. De La Cruz gains from the idea of exclusion, just because you don't literally see it doesn't mean that its not there.
Choose your own dust adventure!
Nobody likes dusting but everyone should find dust interesting.
Working with Jeannie Hunnicutt and with Jen Christiansen's art direction, I created this month's Scientific American Graphic Science visualization based on a recent paper The Ecology of microscopic life in household dust.
Barberan A et al. (2015) The ecology of microscopic life in household dust. Proc. R. Soc. B 282: 20151139.
A very large list of named colors generated from combining some of the many lists that already exist (X11, Crayola, Raveling, Resene, wikipedia, xkcd, etc).
For each color, coordinates in RGB, HSV, XYZ, Lab and LCH space are given along with the 5 nearest, as measured with ΔE, named neighbours.
I also provide a web service. Simply call this URL with an RGB string.
It is possible to predict the values of unsampled data by using linear regression on correlated sample data.
This month, we begin our column with a quote, shown here in its full context from Box's paper Science and Statistics.
In applying mathematics to subjects such as physics or statistics we make tentative assumptions about the real world which we know are false but which we believe may be useful nonetheless. The physicist knows that particles have mass and yet certain results, approximating what really happens, may be derived from the assumption that they do not. Equally, the statistician knows, for example, that in nature there never was a normal distribution, there never was a straight line, yet with normal and linear assumptions, known to be false, he can often derive results which match, to a useful approximation, those found in the real world.
—Box, G. J. Am. Stat. Assoc. 71, 791–799 (1976).
This column is our first in the series about regression. We show that regression and correlation are related concepts—they both quantify trends—and that the calculations for simple linear regression are essentially the same as for one-way ANOVA.
While correlation provides a measure of a specific kind of association between variables, regression allows us to fit correlated sample data to a model, which can be used to predict the values of unsampled data.
Altman, N. & Krzywinski, M. (2015) Points of Significance: Simple Linear Regression Nature Methods 12:999-1000.
Altman, N. & Krzywinski, M. (2015) Points of significance: Association, correlation and causation Nature Methods 12:899-900.
Krzywinski, M. & Altman, N. (2014) Points of significance: Analysis of variance (ANOVA) and blocking. Nature Methods 11:699-700.
Correlation implies association, but not causation. Conversely, causation implies association, but not correlation.
This month, we distinguish between association, correlation and causation.
Association, also called dependence, is a very general relationship: one variable provides information about the other. Correlation, on the other hand, is a specific kind of association: an increasing or decreasing trend. Not all associations are correlations. Moreover, causality can be connected only to association.
We discuss how correlation can be quantified using correlation coefficients (Pearson, Spearman) and show how spurious corrlations can arise in random data as well as very large independent data sets. For example, per capita cheese consumption is correlated with the number of people who died by becoming tangled in bedsheets.
Altman, N. & Krzywinski, M. (2015) Points of Significance: Association, correlation and causation Nature Methods 12:899-900.
For making probabilistic inferences, a graph is worth a thousand words.
This month we continue with the theme of Bayesian statistics and look at Bayesian networks, which combine network analysis with Bayesian statistics.
In a Bayesian network, nodes represent entities, such as genes, and the influence that one gene has over another is represented by a edge and probability table (or function). Bayes' Theorem is used to calculate the probability of a state for any entity.
In our previous columns about Bayesian statistics, we saw how new information (likelihood) can be incorporated into the probability model (prior) to update our belief of the state of the system (posterior). In the context of a Bayesian network, relationships called conditional dependencies can arise between nodes when information is added to the network. Using a small gene regulation network we show how these dependencies may connect nodes along different paths.
Puga, J.L, Krzywinski, M. & Altman, N. (2015) Points of Significance: Bayesian Statistics Nature Methods 12:277-278.
Puga, J.L, Krzywinski, M. & Altman, N. (2015) Points of Significance: Bayes' Theorem Nature Methods 12:277-278.